Papers with English and German
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle (N19-1)
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| Challenge: | Discontinuous constituency trees are derivations of Linear Context-Free Rewriting Systems (LCFRS), which makes them much harder to parse. |
| Approach: | They propose a transition system that uses a set of parsing items with constant-time random access instead of storing subtrees in a stack . |
| Outcome: | The proposed system constructs a discontinuous constituency tree in 4n–2 transitions for a sentence of length n. |
University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task (D19-56)
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| Challenge: | University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with English and German as targeted languages. |
| Approach: | The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG . they submitted a multilingual system based on the Content Selection and Planning model . |
| Outcome: | The University of Edinburgh participated in all six tracks with English and German as target languages. |
A Document-Level Text Simplification Dataset for Japanese (2024.lrec-main)
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| Challenge: | Document-level text simplification tasks combine summarization and intra-sentence simplification. |
| Approach: | They devised a Japanese document-level text simplification dataset based on newspaper articles and Wikipedia. |
| Outcome: | The proposed dataset compared Japanese document-level text simplification models with English models and newspaper articles. |
Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection (C18-1)
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| Challenge: | idioms and metaphors are often studied in isolation, challenging the distinction . e.g., metaphorical concept mappings are ubiquitous in everyday life, thus they are ubiquitous . |
| Approach: | They propose to view the detection problem as a generalized non-literal language classification problem. |
| Outcome: | The proposed model improves on four metaphor and idiom detection tasks in two languages, English and German. |
QUD-Based Annotation of Discourse Structure and Information Structure: Tool and Evaluation (L18-1)
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| Challenge: | a new annotation scheme and discourse-analytic method is developed for information structure annotation. |
| Approach: | They propose a new annotation scheme and a discourse-analytic method based on Questions under Discussion . they introduce a tool which enables the analyst to semi-automatically segment texts and enhance them with QUDs . |
| Outcome: | The proposed method achieves good inter-annotator scores and good agreement with discourse annotations. |
StatBot.Swiss: Bilingual Open Data Exploration in Natural Language (2024.findings-acl)
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Farhad Nooralahzadeh, Yi Zhang, Ellery Smith, Sabine Maennel, Cyril Matthey-Doret, Raphaël De Fondeville, Kurt Stockinger
| Challenge: | StatBot.Swiss dataset is the first bilingual benchmark for evaluating Text-to-SQL systems based on real-world applications. |
| Approach: | They propose to use a bilingual dataset to evaluate LLMs in Text-to-SQL systems. |
| Outcome: | The proposed dataset contains 455 natural language/SQL-pairs over 35 big databases with varying level of complexity for English and German. |
Curation of Benchmark Templates for Measuring Gender Bias in Named Entity Recognition Models (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) models are susceptible to gender bias . benchmark datasets are curated specifically for a given NLP task . |
| Approach: | They propose to filter out benchmark templates with a higher probability of detecting gender bias in NER models. |
| Outcome: | The proposed method is based on masked token prediction and tested in English and german using the corresponding fine-tuned BERT base model. |
Evaluating Readability Metrics for German Medical Text Simplification (2025.coling-main)
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| Challenge: | Clinical reports and scientific information sources are written for medical experts, preventing patients from understanding the main messages of these texts. |
| Approach: | They evaluated the suitability of 18 statistical, part-of-speech-based, syntactic, semantic and fluency metrics to measure readability of German medical texts. |
| Outcome: | The proposed measures are compared with standard methods on English medical texts and simplified summaries. |
Split or Merge: Which is Better for Unsupervised RST Parsing? (D19-1)
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| Challenge: | Rhetorical Structure Theory (RST) parsers have been based on supervised learning approaches that require an annotated corpus of sufficient size and quality. |
| Approach: | They propose two unsupervised methods that build an optimal RST tree based on a dissimilarity score function for splitting a text span into smaller ones and a similarity score for merging two adjacent spans into a large one. |
| Outcome: | The proposed method achieves the best score on English and German RST treebanks, around 0.8 F1 score, close to the previous supervised parsers. |
SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading (2024.emnlp-main)
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Tu Dinh, Carlos Mullov, Leonard Bärmann, Zhaolin Li, Danni Liu, Simon Reiß, Jueun Lee, Nathan Lerzer, Jianfeng Gao, Fabian Peller-Konrad, Tobias Röddiger, Alexander Waibel, Tamim Asfour, Michael Beigl, Rainer Stiefelhagen, Carsten Dachsbacher, Klemens Böhm, Jan Niehues
| Challenge: | Large Language Models (LLMs) are rapidly developing and are becoming more and more useful in scientific tasks. |
| Approach: | They propose to use LLM-as-a-judge to grade LLMs on SciEx to assess their ability on scientific tasks. |
| Outcome: | The proposed benchmarks show that the LLMs perform decently on free-form exams, achieving 0.948 Pearson correlation with expert grading. |